The Power of People - How Successful Organizations Use Workforce Analytics To Improve Business Performance

1. 1Name the business outcome first, in the executive's terms: profitability, revenue, sales growth, cost savings, market share, or value to a constituent such as the taxpayer. Write it as one measurable outcome before you touch any HR data. The book's premise is that workforce analytics exists to improve business performance, so a project with no outcome named has no destination.
2. 2Find where performance varies and start there. Rentokil Initial's trigger was variable sales results and turnover across 700 global salespeople: some regions overachieved targets easily while others consistently underachieved. Variation between comparable units is the cleanest place to ask what the workforce explains.
3. 3Replace anecdote with a testable question. CEO Alan Brown was getting anecdotal explanations from managers and directors and chose an analytical approach instead. He held no assumptions, but because everyone pointed to people-related topics, he focused there. Write down each competing explanation you hear and turn it into a hypothesis the data can confirm or reject.
4. 4Build the chain from the HR practice to the HR metric to the business outcome outside HR. ISS linked efforts to raise engagement to customer satisfaction and, ultimately, contract profitability. State each link in writing, and make sure the last link is a financial or operational result a non-HR executive already tracks.
5. 5Listen to sponsors before you commit. Don't dive into the first project requested. Talk to the people who gave you the job and to potential sponsors to learn which outcome they care about, and take time to think through where you want to be in six months, one year, and three years.
6. 6Involve the executives who own the outcome throughout the project, not only at the end. At ISS the executives were involved throughout, so the C-suite presentation by the consultant was well received and the recommendations were readily accepted.
7. 7Close the loop with action. In the eight-step methodology, stakeholder communication is Step 7 and implementing actions is Step 8. ISS acted on the engagement drivers the analysis identified. A finding that does not change a practice leaves business performance unchanged.
8. 8Extend to adjacent outcomes once the first link holds. Encouraged by the result, ISS began exploring links between engagement and sickness rates and customer churn. Add the next outcome only after the first chain is credible.

ISS Group shows the chain the construct demands. The team linked an HR practice, efforts to increase employee engagement, to business outcomes outside HR: customer satisfaction and, ultimately, contract profitability. ISS identified the drivers of engagement through analytics. Executives were involved throughout the project, so the consultant's presentation to the executive group was well received and the recommendations were readily accepted. Stakeholder communication (Step 7) built commitment, and ISS then acted on the engagement drivers (Step 8). ISS credits the project's success to this link between HR practices and a contract-level profit result. The team is now testing engagement against sickness rates and customer churn. Rentokil Initial shows the starting condition. A CEO facing highly variable results across 700 salespeople, with some regions overachieving and others consistently underachieving, chose data over managers' anecdotes. The passages end before Rentokil's findings, so take only the framing from it: begin with measured variation in a business outcome.

• If a sponsor asks for a project that cannot be tied to a named business outcome (profit, revenue, sales growth, cost savings, market share, or value to constituents), then pause and reframe it before accepting.
• If performance varies sharply between regions or units while managers offer conflicting stories, then treat the variation as the project and test the people-related explanations with data.
• If your result stops at an HR metric such as engagement, then extend the chain to a business outcome, as ISS did with customer satisfaction and contract profitability.
• If executives who own the outcome have not been involved before the readout, then bring them in earlier, because ISS's involved executives accepted the recommendations readily.
• If the first chain is established and credible, then explore adjacent outcomes (sickness rates, customer churn) rather than starting from scratch.

• Reporting only HR metrics. The tell: the final slide shows engagement or turnover and no revenue, profit, or cost figure.
• Accepting anecdote as the explanation. The tell: the project's hypotheses come from manager opinions and nobody has defined what data would disprove them.
• Jumping into the first requested project. The tell: no one can say which sponsor outcome the work serves or what the six-month to three-year direction is.
• Presenting to executives for the first time at the end. The tell: the readout triggers debate about the question itself rather than about the actions.
• Stopping at insight. The tell: there is no owner or plan for acting on the drivers, so Step 8 never happens and the outcome metric does not move.

• ▸Business outcome: name the one financial or operational result (profit, revenue, sales growth, cost savings, market share, or constituent value) the project must move.
• ▸Outcome owner/sponsor: name the executive accountable for that outcome and note when you last listened to their priorities.
• ▸Performance variation observed: describe where the outcome differs across regions, units, or teams (as with Rentokil's 700 salespeople), with the data source.
• ▸Competing explanations heard: list the anecdotes from managers and directors, one per line.
• ▸Hypotheses to test: restate each people-related explanation as a question the data can answer.
• ▸Chain of links: HR practice → HR metric → non-HR business outcome, as in ISS's engagement → customer satisfaction → contract profitability.
• ▸Executive involvement plan: list which executives you will involve and at which stages, before the final readout.
• ▸Action and owner (Step 8): state what will change in practice, who owns it, and when.
• ▸Next adjacent outcome: name one follow-on link to explore (for example sickness rates or customer churn) once the first holds.

1. 1Sequence the build the way the book does: direction first (Part II: set direction, engage stakeholders, get a quick win), then capability (Part III: data, technology, team, partners, operating model), then culture (Part IV). Do not buy technology or hire before you have a direction.
2. 2Set direction before you set scope. Listen to prospective project sponsors, assess demand with the Seven Forces of Demand (Chapter 7), agree the scope of analytics, and write a vision and mission statement. The book puts technology selection after vision and mission ('Starting with Vision and Mission'), so the statement is your filter for every later tooling and hiring decision.
3. 3Secure a project sponsor for every project, and run each project through the Eight Step Model for Purposeful Analytics (Chapter 4). The model exists to link an analytics project to a business problem. Before starting any project, write one sentence naming the business problem and the sponsor who owns it. If you cannot, do not start.
4. 4Map stakeholders in the book's three groups: those you serve, those you depend on, and those you impact. Name an individual in each group and decide what each needs from you. Bring Finance along early (the 'Bring Finance Along with You' vignette), because they are a dependency as well as an audience.
5. 5Choose a first project with the Complexity-Impact Matrix (Chapter 9). Score each candidate on complexity and on impact, and pick one that is quick to deliver and visible to the sponsor. This builds credibility for the harder work that follows.
6. 6Know your data pragmatically. Inventory data types and sources, name the data quality problems, and set data governance. Build a data dictionary, which the vignette credits with bringing credibility. Do not wait for one integrated HRIS; the vignette 'Don't Let the Lack of One Integrated HRIS Stop You' says to proceed without it.
7. 7Build the team against the Six Skills for Success (Chapter 12), then configure team roles. A blend of skills works better than a single profile, and data privacy skills deserve a deliberate investment. Anticipate business needs when you plan roles rather than reacting to requests.
8. 8Fill skill gaps through a partner decision. Compare the options for building the team and choose among them. Start small and keep the partnership focused. Use an external partner when time to value matters. Then fix the operating model across four elements: strategy, governance, implementation, accountability.

The source passages give the book's structure and chapter and case titles, not full text, so this example uses only that structure and is an illustration, not a reported case. The book lists a case, 'From Employee Engagement to Profitability at ISS Group', which shows the shape of a purposeful project: it starts from a business outcome (profitability) and works back to a people measure (engagement). Apply that shape. Step 1: a sponsor names the business problem in one sentence. Step 2: you run it through the Eight Step Model so the analytics question is tied to that problem. Step 3: you check the data you have against your data dictionary and governance rules instead of waiting for a perfect integrated system. Step 4: you confirm the team covers the Six Skills, using a partner if a skill is missing. Step 5: you present the result to the sponsor and to Finance. The same logic appears in the other listed cases: Rentokil Initial (sales), the Metropolitan Police (value to the taxpayer), Westpac (well-being), and Nielsen (retention). Each is framed around a business or organizational outcome rather than around an analytical technique.

• If you cannot name a sponsor and a business problem for a proposed project, then do not start it; the Eight Step Model assumes both.
• If two candidate projects look equally useful, then pick the one that scores lower on complexity and higher on impact in the Complexity-Impact Matrix, because the first project must win credibility.
• If your HR data sits in several systems rather than one integrated HRIS, then start with what you have and document it in a data dictionary instead of delaying.
• If you have a technology vendor choice to make and no vision and mission statement, then write the statement first and judge the vendor against it. Likewise, if reporting tools cannot drill down, treat that as a selection criterion ('Drillability Is Key').
• If a needed skill is missing from the Six Skills, then decide whether to build or partner; if speed matters, use an external partner on a small, focused scope.

• Starting with technology or a tool purchase before direction is set. The tell: you can list platforms but cannot state the vision and mission in a sentence.
• Running projects without a sponsor. The tell: the output is delivered and no executive acts on it, or no one can say who asked for it.
• Treating data perfection as a precondition. The tell: months pass on integration and data cleaning with no deliverable shown to a stakeholder.
• Building a team around one skill type, such as pure statistics. The tell: analyses are technically sound but ignore business context, data privacy, or how to communicate them (the book argues for a blend of skills and for privacy skills).
• Ignoring stakeholders you depend on, such as Finance or data owners. The tell: your numbers are challenged or blocked late in the project, or access to data stalls.

• ▸Business problem (one sentence): State the problem in business terms, not as an analytical question.
• ▸Executive sponsor: Name the person who owns the problem and will act on the result.
• ▸Linked to vision and mission: Say which part of your vision and mission statement this project serves.
• ▸Stakeholders served / depended upon / impacted: List at least one named person in each of the three groups.
• ▸Complexity score and impact score: Rate each, using the Complexity-Impact Matrix, and note whether this is a quick win.
• ▸Data required and source: List the data, where it sits, and whether it is in your data dictionary.
• ▸Data quality and governance issues: Name known quality problems and any privacy or governance constraints.
• ▸Skills coverage (Six Skills for Success): Mark which skills the team has and which need a partner.
• ▸Accountability and next review date: Name who is accountable for delivery and when the sponsor reviews progress.

1. 1Start from a business question, not a dataset. Write the decision in one sentence (e.g., which people will leave, how successful a candidate will be if hired, what workforce the business needs in future) and name who owns it. The source says the priority has shifted from gathering and reporting data to making sense of it and getting insights to those who decide.
2. 2Split the question the way Ericsson's Salvador Malo does: first translate business requirements into future needs, then get to know your workforce in some depth. Do both before recommending anything. Business-side insight alone ignores the people; workforce-side insight alone ignores the business.
3. 3Set up the logic before the analysis. Use a framework such as LAMP (logic, analytics, measure, process): state the logic linking the HR practice to a performance outcome, choose the analysis, define the measure, and map the process by which the result changes an action. Literature cited in the source shows HR practices affect performance at the individual, team, unit, and organization level, so specify which level you are targeting.
4. 4Choose leading and lagging indicators on purpose. Tracy's guidance is to give leaders both types of people data and to run an 'insights' exercise rather than a reporting exercise. For each indicator, write down the action it would trigger and who would take it.
5. 5Isolate the behaviors or drivers that separate outcomes, then build the decision rule on them. Rentokil Initial isolated the key behaviors of high-performing sales professionals and used automated assessment to select candidates on those behaviors. Define the behavior, test it against the outcome, and only then embed it in the process.
6. 6Ask the tough questions before presenting results (Alec Levenson). What would change your mind? What else explains this? Treat the analysis as input to executives' fiduciary duty to manage the organization, not as decoration for a decision already made.
7. 7Deliver the insight to the decision-maker in a simple story and visualization, ideally summarized in one sentence. Pair it with a specific recommended action, owner, and date, and do not hand over a report.
8. 8Close the loop. Track the business outcome (sales, profit, retention) against the decision, and record whether the analysis or the intuition it replaced turned out right. Keep ethical limits in view: decide in advance what should and should not be measured and reported about employees.

Rentokil Initial wanted to improve the predictability of sales success. Rather than rely on managers' impressions of who makes a good salesperson, the project isolated the key behaviors of its high-performing sales professionals. It then used automated assessment techniques to select future candidates based on those behaviors. The decision, who to hire, moved from anecdote to evidence about observed behavior. The source reports that global sales rose more than 40 percent and that the project returned more than 300 percent on investment. The lesson is that the analysis produced a change in a hiring process and was judged on a business outcome, not on report quality. The source describes this in more detail in its Chapter 6 case studies.

• If the question is only 'what happened,' then it is reporting; rework it into an insights question tied to an action and a business outcome, as Tracy advises.
• If you cannot name the decision-maker and the action the result would trigger, then stop and do not run the analysis yet.
• If the evidence conflicts with a leader's intuition, then test and ask the tough questions rather than overriding or deferring. Report both the finding and its limits.
• If the metric touches individual employees (such as a risk-of-leaving index), then set ethical limits on measurement and reporting before using it, and route it to managers as a prompt for preventive action.
• If the analysis shows a pattern in the data but nobody acts on it, then fix delivery (story, visualization, simplicity) before collecting more data, because the limit today is seldom data or computing power.

• Reporting instead of insight: dashboards of HR metrics (such as time to hire) circulate but no decision changes. The tell: no one can say what action last quarter's report triggered.
• Treating HR analytics as only HR's own scorecard. HR KPIs hold HR accountable but are not the business-outcome analytics that matter most. The tell: all metrics measure HR process and none link to profit, sales, or customer results.
• Going purely analytical and losing the human element. The authors warn against HR losing touch with the human behaviors and characteristics that help people and businesses succeed. The tell: decisions cite a model score with no conversation with the people affected.
• Gathering more data rather than making sense of it. The tell: new data sources are added while the same unanswered question sits open.
• Leaders staying passive and expecting analytics to hand over answers, or defaulting to program-driven habits like the annual salary cycle and talent review. The tell: analytics is only asked for after a decision is made, to justify it.

• ▸Business decision: state the choice to be made in one sentence.
• ▸Decision owner and date: name the person and the deadline.
• ▸Business need and workforce picture: summarize future business requirements, then what you know about the workforce in depth.
• ▸Logic link: write how the HR practice is expected to affect which outcome and at what level (individual, team, unit, organization).
• ▸Leading and lagging indicators: list each with the action it would trigger.
• ▸Evidence and its limits: summarize the finding, the tough questions asked, and alternative explanations tested.
• ▸Intuition or anecdote being tested: record the prior belief and whether the data supports it.
• ▸Ethical limits: state what will not be measured or reported about employees.
• ▸One-sentence story and recommended action: give the simple message, owner, and next step.
• ▸Outcome review: record the business result (sales, profit, retention) and the review date.

1. 1Start with the business priority, not the program. Before designing any intervention, get written agreement on why the outcome (for example, engagement) matters to the business and what return you expect. ISS did this as Step 1, so the analytics work had a defined investment case.
2. 2Test the assumed link in your own organization before you invest. ISS found substantial evidence in the literature linking engagement and performance, yet still checked for the association internally before making a considerable investment. State 1-2 explicit hypotheses (ISS: engagement is positively related to employee and customer experience) so data collection and analysis stay focused.
3. 3Build the project team around the stakeholders who must act on the result. ISS included Group HR, Group Marketing, and an external consultancy partner who already knew senior leaders. Involve them from the outset, not when results are presented, so buy-in and resources are in place.
4. 4Choose the strongest research design the setting allows. The hierarchy runs randomized experiment, quasi-experimental, correlational, then qualitative. If you cannot randomize (two people on the same team cannot easily be treated differently), deliver the intervention to different business units and compare against a control group.
5. 5Plan for the confounds your design cannot remove. In a quasi-experiment, different managers giving different levels of recognition could explain engagement differences. Name these factors up front and watch for them in the results.
6. 6Measure repeatedly over time. A single post-intervention reading cannot show how long an effect lasts, whether it strengthens or weakens, or whether it reverses. Use a longitudinal design so you can decide whether to continue, adjust, or stop.
7. 7Check the recommendation against local context before rollout. Aviva's Sally Dillon keeps analyses general but makes implementation recommendations specific to unit, team, and sometimes individual level, and checks each idea before implementation to avoid unintended consequences.
8. 8Replicate before you scale. Quasi-experimental results support robust conclusions when they replicate across multiple studies. Treat the first result as enough to continue the intervention, not as proof it should be applied everywhere.

ISS, a company whose strategy depends on highly engaged employees, wanted to know whether investing in engagement would pay off. Group Head of Marketing Peter Ankerstjerne sponsored a workforce analytics project on the relationships among employee engagement, customer experience, and profitability. Simon Svegaard, Group Business Analytics Manager, explained that although the literature shows a positive engagement-performance link, ISS wanted to see that association in its own data before making considerable investment. The team first confirmed the business priority of engagement, so a return could be judged. It then formed a project team from Group HR, Group Marketing, and an external consultancy, including a consultant who had worked with senior ISS leaders before. Collaboration began at the start rather than at the results presentation, which made buy-in, support, and resources easier to secure. The team then wrote two hypotheses, the first being that engagement is positively related to both employee and customer experience, and these guided data collection and analysis. The result is an intervention decision grounded in the company's own evidence, with the key stakeholders already committed.

• If you can randomly assign employees to intervention and control groups, do it; if you cannot, use a quasi-experiment across business units with a control group and treat the conclusion as less certain.
• If only correlational data exists, use strong, large-sample correlations to guide direction but do not claim causation; confirm with a pilot before a large investment.
• If the effect has been measured only once, do not decide on lasting impact; add repeated measurement before scaling.
• If published research says an intervention works, still test the underlying association in your own organization before committing major spend.
• If a recommendation will play out differently at unit, team, or individual level, tailor and pre-check the implementation with the business before launch.

• Trusting your gut or generic literature and launching a program without checking your own data; the tell is an investment case that cites only external studies.
• Involving stakeholders only when results are presented; the tell is skepticism or no resources appearing at the readout.
• Running sophisticated analysis on a weak design; the tell is a polished model that cannot say whether the intervention caused the outcome.
• Treating a quasi-experimental difference as clean causation while ignoring confounds such as managers' differing recognition; the tell is that no one listed alternative explanations.
• Working from an analytical ivory tower and ignoring local context; the tell is a recommendation that produces unintended consequences at team or individual level.

• ▸Business priority and expected return: State why the target outcome matters to strategy and what return would justify investment.
• ▸Sponsor and project team: List the sponsor and representatives from HR, the affected business function, and any external partner.
• ▸Hypotheses: Write 1-2 explicit, testable statements linking the intervention to the outcome.
• ▸Internal evidence check: Note how you will confirm the association in your own data before major spend.
• ▸Research design: Choose randomized, quasi-experimental, or correlational, and justify why.
• ▸Control or comparison group: Define who receives the intervention and who does not.
• ▸Known confounds: List factors (such as manager recognition differences) that could explain results.
• ▸Measurement schedule: Set the repeated time points for the outcome measure.
• ▸Context check and decision: Record unit/team-level implementation checks and the continue, adjust, or stop criteria.

1. 1Define the two halves separately before you measure anything. Engagement covers motivation, purpose and capability. Well-being covers work-related stress and distress. Write one measurable indicator list for each so a single blended score cannot hide a split, such as high motivation alongside high distress.
2. 2Pair what people say with what they do. Alan Wild (IBM) frames the goal as discerning what people tell you by their actions, not only by what they say they would do. For each survey-based indicator, name one behavioral or system-data counterpart, such as the risk-of-leaving signal, and compare the two.
3. 3Mine the open-ended survey comments. Text-mine them for sentiment, converting the language into a numerical representation. After that, the analysis objectives are the same as for any quantitative data. Sadat Shami (IBM) notes that text, including social media, can carry signals from which an employee's engagement level can be inferred.
4. 4Run a qualitative pass to explain the numbers. Use interviews or focus groups to find the mechanism behind a pattern. The passages' example is a focus group revealing that managers are not meeting new hires in the first week.
5. 5Contextualize for the audience. Add verbatim quotes from interviewees to the quantitative result. Turn the project into a story told from the perspective of the people affected by the recommended action.
6. 6Link engagement and well-being to business outcomes. Source material shows HR practices affect performance at the individual, team, unit and organization levels. State which outcome (for example retention or profitability) the finding is tied to, so sponsors see why it matters.
7. 7Set ethical limits before you collect. The source flags emerging ethical issues requiring limits on what should and should not be measured and reported about employees. Document what you will not infer from social or text data, and who sees individual-level risk indicators.
8. 8Check every variable for adverse impact. A proxy variable can be measured accurately and still disadvantage a group, as postcode can proxy for socioeconomic status. Observing a difference alone cannot tell you whether it is genuine or bias, so test before acting on any group-level gap.
9. 9Deliver the insight to someone who can act. The source says the priority has shifted from gathering data to finding the pivotal stories and getting insights to decision makers. Each finding ends with a named owner and a preventive action.

The passages name two relevant case studies, ISS Group (engagement to profitability) and Westpac (predictive analytics for well-being), but give no results for either, so this example is an illustration built only from techniques the passages describe. Suppose open-ended survey comments from one unit are text-mined and show a negative sentiment cluster among recent joiners. The sentiment score shows what the pattern is but not why. A follow-up focus group with those joiners reveals that managers are not meeting with new hires in the first week, the very finding the source cites as a focus-group payoff. You report the sentiment result with verbatim quotes from the joiners, so leaders hear the experience behind the metric. You compare the sentiment signal with a behavioral signal, the risk-of-leaving index, to see whether what people say matches what they do. The recommended action is a first-week manager meeting, with the unit's manager as owner. Before reporting by demographic group, you check whether any variable acts as a proxy for a protected characteristic.

• If a survey score and a behavioral signal disagree, then trust neither alone: investigate with qualitative work, since the stated and enacted views carry different information.
• If a quantitative result lacks a believable cause, then run interviews or focus groups before recommending action, because they surface mechanisms such as skipped manager contact.
• If you are presenting to HR or business leaders, then add verbatim quotes and frame the story around those affected, so the technical result lands.
• If a measure could reveal individual distress or serve as a proxy for a protected group, then set the limits on measuring and reporting it before collecting data.
• If a group difference appears in a score, then do not assume bias or genuine difference until you test, because impact alone cannot distinguish them.

• Reporting one blended engagement-and-well-being number. The tell: scores look healthy while the stress or distress items are never discussed separately.
• Relying only on what people say they would do. The tell: survey intent never gets checked against actual behavior such as attrition.
• Presenting sentiment scores with no explanation. The tell: leaders ask 'so what do we do?' and no one can name the mechanism behind the number.
• Collecting everything because the data and computing power exist. The tell: no written statement of what will not be measured or reported about employees.
• Using a variable that is accurately measured but acts as a proxy, such as postcode, and then reading group differences as true differences. The tell: the pattern tracks demographic lines and no bias check was run.

• ▸Engagement indicators: list the measures of motivation, purpose and capability you will track.
• ▸Well-being indicators: list the measures of work-related stress and distress, kept separate from engagement.
• ▸Stated signals: which survey items or open-ended comments you will text-mine for sentiment.
• ▸Behavioral signals: the matching action-based data, for example a risk-of-leaving index, to compare against the stated signals.
• ▸Qualitative follow-up: the interview or focus group plan to explain any surprising pattern.
• ▸Verbatim quotes: the quotes selected to contextualize the quantitative result for the audience.
• ▸Business outcome link: the performance outcome this finding is tied to, and the level (individual, team, unit, organization).
• ▸Ethical limits: what will not be measured, inferred or reported, and who may see individual-level indicators.
• ▸Bias check: variables screened for proxy effects or adverse impact, and the result.
• ▸Action owner and preventive action: the person who receives the insight and the specific step they take.

1. 1Name the outcome in one line before touching data. Split the construct into two measurable outcomes: performance (individual or team, such as sales or productivity) and voluntary retention (who chooses to leave). Each gets its own definition, population, and time window. Use the project-sponsor question from the book's purposeful-analytics model: which business decision changes if this number moves?
2. 2Separate voluntary from involuntary exits at the start. The construct covers only employees who choose to leave, so mixing in dismissals, restructuring, or retirements contaminates the retention outcome. Record the exit-reason field and its source.
3. 3Measure what people do, not what they say they would do. Alan Wild (IBM) frames the aim as discerning intentions from actions. Build the retention outcome from actual exit records and the performance outcome from recorded results, such as sales or output. Treat survey intent as a predictor to test, not as the outcome.
4. 4Pick the objective of analysis explicitly: describe the current pattern, explain what drives it, or predict who is likely to leave or underperform. The book treats objectives of analysis and research design as separate steps. The objective determines the data you need and the model you use.
5. 5Match the method to the objective. Use traditional statistics when you must explain a driver to a sponsor. Consider machine learning when prediction accuracy is the goal. State up front which you chose and why.
6. 6Check for algorithmic side effects before release. The book lists the social consequences of algorithms as a core topic. Review whether a retention-risk or performance model would treat groups unfairly or expose individuals, and decide who sees individual-level output.
7. 7Link outcomes in a chain where the data allows it. The ISS Group case title (employee engagement to profitability) and the Rentokil Initial case (growing sales) show the pattern: connect a workforce measure to a business result, not just to another HR metric.
8. 8Close with a named action owner. The book asks why analytics projects fail. Before reporting, write down what the sponsor will do differently if the finding holds, and when you will check the result.

The passages give case titles but not their findings, so no numbers are claimed here. They name three cases that match this construct. 'Improving Careers Through Retention Analytics at Nielsen' addresses retention. 'Growing Sales Using Workforce Analytics at Rentokil Initial' addresses a performance outcome, sales. 'From Employee Engagement to Profitability at ISS Group' links a workforce measure to a business result. Use them as a template. For a Nielsen-style retention project, define voluntary exit as the outcome, choose prediction as the objective, and use action-based exit records. Then ask what career or management action the sponsor will take for employees flagged as likely to leave. For a Rentokil-style sales project, define sales per person or team as the outcome, choose explanation as the objective, and test which workforce factors move it. Before building either, read the full case chapters (Chapter 6) for the actual methods and results, and do not borrow their conclusions for your own organization.

• If the sponsor wants to know why people leave, then choose an explanatory design and conventional statistics. If they want to know who will leave next, then choose a predictive objective and consider machine learning.
• If an exit is not clearly the employee's choice, then exclude it from the voluntary-retention outcome and log it separately.
• If the only available performance measure is a self-report or an intention survey, then label it a predictor and keep a recorded behavioral result as the outcome.
• If a model will produce individual-level risk scores, then run a fairness and access review before sharing it, and restrict who sees names.
• If no one can say what action follows a finding, then stop and re-scope with the sponsor before analyzing.

• Mixing voluntary and involuntary exits in one retention rate. The tell: the rate moves after a layoff and no one can explain the change.
• Treating stated intent as behavior. The tell: survey 'likely to leave' scores are reported as the retention outcome, yet actual departures differ.
• Analysis with no sponsor decision attached, one of the project-failure patterns the book flags. The tell: a finished report that no one acts on.
• Choosing a method by fashion rather than objective. The tell: a machine-learning model is built when stakeholders asked for an explainable driver, or the reverse.
• Ignoring the social consequences of the algorithm. The tell: a risk list circulates with no rule on who may see it or how it is used with individuals.

• ▸Business decision at stake: state the decision that changes if the number moves.
• ▸Sponsor and action owner: name the person who will act on the finding.
• ▸Performance outcome: define the metric (e.g., sales, productivity), level (individual or team), and time window.
• ▸Retention outcome: define voluntary exit, the population, and the window; list excluded exit types.
• ▸Data source for each outcome: record the system and whether it captures actions or self-reports.
• ▸Objective of analysis: choose describe, explain, or predict, with a one-line reason.
• ▸Method choice: name traditional statistics or machine learning and why it fits the objective.
• ▸Fairness and access check: note who could be affected unequally and who may see individual-level output.
• ▸Action trigger and review date: specify what happens if the finding holds and when you will check results.